Paper Citation Record · LEDGER

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks

As of 16 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:1908.06378.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.06378 v3

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:51:25.094345Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation c346859e-e3b7-467b-920b-41b88ff7c852 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Deep Learning using Rectified Linear Units (ReLU)

Reference 1

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Observation 0f58f514-cf29-49ee-b8b7-20b80a285f4e · outbound

This paper cites Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip

Reference 2

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Observation 4434ba48-73be-4966-a5bf-a8fdaa65d438 · outbound

This paper cites Feature representations for neuromorphic audio spike streams.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Feature representations for neuromorphic audio spike streams

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.545467Z

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Observation e00d365d-9e7a-445a-8c80-b6a5b3211149 · outbound

This paper cites Long short-term memory and learning-to-learn in networks of spiking neurons.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Long short-term memory and learning-to-learn in networks of spiking neurons

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.534287Z

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Observation b0402214-e856-4a41-99d0-6f959c248fc0 · outbound

This paper cites Error-backpropagation in temporally encoded networks of spiking neurons.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Error-backpropagation in temporally encoded networks of spiking neurons

Reference 5

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Observation 45108c42-1837-4706-9b1f-440c86887e90 · outbound

This paper cites A unified architecture for natural language processing: Deep neural networks with multitask learning.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks A unified architecture for natural language processing: Deep neural networks with multitask learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.513592Z

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Observation c35ee156-ae40-4820-931c-6c2328ae34ed · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Loihi: A neuromorphic manycore processor with on-chip learning

Reference 7

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Observation 71e0a9e5-f24b-4851-a528-7bac4b052b8e · outbound

This paper cites Unsupervised learning of digit recognition using spike-timing-dependent plasticity.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Unsupervised learning of digit recognition using spike-timing-dependent plasticity

Reference 8

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Observation 2d744dc7-d491-43ff-826d-3ff8e9ea211a · outbound

This paper cites Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.480144Z

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Observation 736ae489-4cf7-4c3b-9491-a0aa54b87246 · outbound

This paper cites Backpropagation for energy-efficient neuromorphic computing.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Backpropagation for energy-efficient neuromorphic computing

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.467997Z

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Observation 24844b6f-c41a-46e4-a3b8-a63f929c1776 · outbound

This paper cites Spiking neuron models: Single neurons, populations, plasticity.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Spiking neuron models: Single neurons, populations, plasticity

Reference 11

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Observation 29d4822f-8890-46fe-951b-12e8beb1d876 · outbound

This paper cites Neuro-inspired speech recognition with recurrent spiking neurons.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Neuro-inspired speech recognition with recurrent spiking neurons

Reference 12

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Observation 2b59c47a-e7a3-4a12-9ba6-8f1edbe95719 · outbound

This paper cites Deep learning.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Deep learning

Reference 13

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Observation e25570cf-31ef-4bff-b43a-2eedba47710f · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups

Reference 14

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Observation 47108aa3-1a80-494e-8299-0fd69b83990b · outbound

This paper cites Gradient descent for spiking neural networks.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Gradient descent for spiking neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.419573Z

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Observation c2ee54f0-9f8e-450f-80cf-6a7b418f5563 · outbound

This paper cites Spiking Deep Networks with LIF Neurons.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Spiking Deep Networks with LIF Neurons

Reference 16

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Observation 807bc01d-988b-4a1b-baa0-1126fc0d9171 · outbound

This paper cites Large-scale model of mammalian thalamocortical systems.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Large-scale model of mammalian thalamocortical systems

Reference 17

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Observation 0dd2c7be-11b1-41a0-81dd-f036691df8a5 · outbound

This paper cites Ap-stdp: A novel self-organizing mechanism for efficient reservoir computing.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Ap-stdp: A novel self-organizing mechanism for efficient reservoir computing

Reference 18

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Observation 2eff2346-b0d0-4919-8989-245267c9f8b9 · outbound

This paper cites Hybrid macro/micro level backpropagation for training deep spiking neural networks.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Hybrid macro/micro level backpropagation for training deep spiking neural networks

Reference 19

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Observation bacc3aae-68b3-4459-bbcf-11d1873da6ab · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Adam: A Method for Stochastic Optimization

Reference 20

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Observation 59fc4a76-bf10-46a0-b903-5c83f1c5dd50 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Imagenet classification with deep convolutional neural networks

Reference 21

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Observation cc372e3c-10ed-4b01-9d48-f1b09aa79445 · outbound

This paper cites Deep learning.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Deep learning

Reference 22

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Observation 8ea1d278-261f-487c-8765-0bdbe66d0ba0 · outbound

This paper cites Training deep spiking neural networks using backpropagation.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Training deep spiking neural networks using backpropagation

Reference 23

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Observation 18429281-d52e-45b9-845a-d4c6b5bc1c72 · outbound

This paper cites Tidigits speech corpus.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Tidigits speech corpus

Reference 24

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Observation 5302db57-4aab-4d0a-8f87-dc9b94c794a5 · outbound

This paper cites TI 46-word LDC93S9 , 1991.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks TI 46-word LDC93S9 , 1991

Reference 25

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Observation 27f44aed-1fbb-4add-a641-13d8f9b9e02c · outbound

This paper cites A computational model of filtering, detection, and compression in the cochlea.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks A computational model of filtering, detection, and compression in the cochlea

Reference 26

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Observation f1f21d95-8d04-4c78-8218-0ec1d93e2cb2 · outbound

This paper cites Real-time computing without stable states: A new framework for neural computation based on perturbations.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Real-time computing without stable states: A new framework for neural computation based on perturbations

Reference 27

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Observation 2a8e81b8-a6c5-43a7-a58f-b9fd51aa7c59 · outbound

This paper cites A million spiking-neuron integrated circuit with a scalable communication network and interface.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks A million spiking-neuron integrated circuit with a scalable communication network and interface

Reference 28

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Observation 01a5f660-01cb-4a28-9efb-507b1b212602 · outbound

This paper cites Phenomenological models of synaptic plasticity based on spike timing.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Phenomenological models of synaptic plasticity based on spike timing

Reference 29

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Observation 2ca3a844-c224-493e-a251-97ab53b1f9c5 · outbound

This paper cites Biologically Motivated Algorithms for Propagating Local Target Representations.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Biologically Motivated Algorithms for Propagating Local Target Representations

Reference 30

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Observation 34e160ac-157e-45bb-b74e-dee262c872ac · outbound

This paper cites Supervised learning in spiking neural networks with resume: sequence learning, classification, and spike shifting.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Supervised learning in spiking neural networks with resume: sequence learning, classification, and spike shifting

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.285455Z

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Observation c211b91d-166a-417b-9fcb-75bdf03648d3 · outbound

This paper cites Bsa, a fast and accurate spike train encoding scheme.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Bsa, a fast and accurate spike train encoding scheme

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.274725Z

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Observation afb4c1ac-d716-4f9a-ab04-9a5f89bf20ca · outbound

This paper cites Slayer: Spike layer error reassignment in time.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Slayer: Spike layer error reassignment in time

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.263686Z

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Observation fb3e1723-d5c1-4fd9-98c3-adab938b3a0e · outbound

This paper cites Best practices for convolutional neural networks applied to visual document analysis.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Best practices for convolutional neural networks applied to visual document analysis

Reference 34

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Observation 22975122-0675-454c-8f6e-9e87cd8b32b8 · outbound

This paper cites Spilinc: Spiking liquid-ensemble computing for unsupervised speech and image recognition.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Spilinc: Spiking liquid-ensemble computing for unsupervised speech and image recognition

Reference 35

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Observation f37692e4-492f-43a0-8ef6-70d99cfa7997 · outbound

This paper cites Deep neural networks for object detection.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Deep neural networks for object detection

Reference 36

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1368617c-783c-4011-87e1-cbee99267065 · outbound

This paper cites Backpropagation through time: what it does and how to do it.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Backpropagation through time: what it does and how to do it

Reference 37

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Observation 27432706-0da1-4e81-b97a-2ef01d056ebe · outbound

This paper cites Analysis of liquid ensembles for enhancing the performance and accuracy of liquid state machines.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Analysis of liquid ensembles for enhancing the performance and accuracy of liquid state machines

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.208814Z

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Observation d2782134-600a-4ccb-b9ce-afe6e37c30b8 · outbound

This paper cites Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:51:25.143006Z

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Observation c77b876c-d091-4c12-af10-4adea9c04756 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 40

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Observation 09f0f68c-d1b4-458b-91e9-6dd73c63d822 · outbound

This paper cites A digital liquid state machine with biologically inspired learning and its application to speech recognition.

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks A digital liquid state machine with biologically inspired learning and its application to speech recognition

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:51:25.198335Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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